In recent times, technology has touched almost every part of human life. Generally, there is a lot of scope for research work for further advancement of technology in various fields. Specifically, research inspires innovation and promotes learning in community of scholars, scientists and inventors. Furthermore, there are a number of platforms available that provide data and content needed for carrying out the research work. Conventionally, printed media used to act as source of data needed for the research work. However, with technological evolution digital media has now replaced the printed media as an information source with easy accessibility and improved availability thereof. Additionally, the digital data containing data records may be stored in a database.
Generally, retrieving data spread across various centralized and/or distributed database is performed by using various existing techniques. Specifically, the existing techniques act upon search strings pertaining to a search entity name provided thereto. Subsequently, the existing techniques access the data sources and retrieve most relevant data records from various existing data sources. However, such existing data sources include many identical entity names that are contextually different. Subsequently, such entity names and attributes associated thereto get treated as relevant data records by the existing techniques.
However, the existing techniques suffer with numerous performance issues. Specifically, the existing techniques retrieve data only from homogeneous platforms. Additionally, the existing techniques are unable to extract data from existing data sources having heterogeneous (namely, different) format. Furthermore, data records that are extracted using the existing techniques involve a lot of processing and time complexity as it includes ambiguous data therein. Furthermore, the retrieved data may not essentially belong to specific searched entity name rather to an identical entity name with different intent (namely, resolution). Additionally, the retrieval of data using the existing techniques is complex in nature as it requires human involvement in order to refine the search results containing data records containing many identical entity names and attributes associated therewith. Consequently, such retrieval of data consumes a lot of human effort and time.
Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks associated with the existing techniques of entity name resolution.